Airbnb’s agreement with Amsterdam: some insights from scraped data

Airbnb is under fire. The platform would harm the liveability of Amsterdam neighbourhoods and drive up house prices. In December last year, Amsterdam and Airbnb signed a Memorandum of Understanding (MOU) to deal with abuses. According to Airbnb, the agreement is already bearing fruit. Airbnb thinks Amsterdam should focus its enforcement efforts on other platforms. But Amsterdam wants to introduce a registration requirement for holiday lettings, a measure Airbnb vehemently opposes.

In this article, I analyse some of the changes that occurred since the announcement of the MOU. I use data from Murray Cox (Inside Airbnb) and Tom Slee, who have scraped the Airbnb website various times between May 2014 and May 2017 (scraping is automated retrieval of data from websites). Note that data about Airbnb is always controversial. Read this article to understand why the data collected by Cox and Slee is an important addition to the data provided by the company itself.

Sixty-day limit

Amsterdam residents may rent out their home sixty days per year. According to the new agreement, Airbnb will block advertisements when they exceed that limit. However, this applies only to entire homes and not to rooms, because these may be B&Bs, where the sixty-day cap doesn’t apply.

According to Airbnb, there has been a substantial decrease in the number of homes offered for rent more than sixty days per year. This would be evidence that the MOU is already reducing illegal offerings.

The chart below shows how many rooms and entire homes were available more than sixty days per year, using data from Murray Cox.

The chart appears to confirm what Airbnb has claimed: the number of entire homes available more than sixty days has gone down. However, this started in the first half of 2016, well before the MOU was signed (let alone implemented). So it appears that it was caused by something else.

Perhaps it was the threat of stricter enforcement by the government itself. On 16 February 2016, Amsterdam announced that it was creating its own scraper to collect information from home rental platforms such as Airbnb. In March, the Green Party and Social-Democrats filed motions to step up enforcement.

Room type changes

Does this mean the agreement between Amsterdam and Airbnb wasn’t a turning point? Perhaps it was - but in a different way.

Aggregate data about Airbnb listings are the result of a complex interplay of developments. Some listings are taken off the platform, and new ones are created. In addition, some hosts change the type of their listing - from room to entire home, or vice versa. This is shown in the chart below (using data from Tom Slee).

Until recently, few hosts changed the type of their listings. But since the announcement of the MOU, hundreds of listings have been changed from entire home to room. As indicated above, in the MOU, Airbnb promised to block advertisements for homes that have reached the sixty-day limit. Could it be that people changed their listings into «rooms» to evade the sixty-day limit?

I analysed listings that were changed from home to room between early March and early April 2017 (data from Murray Cox). In early April, over three-quarters of these listings were available more than sixty days per year. This would be consistent with the theory that hosts changed these homes into rooms because of the cap.

It’s possible that these hosts actually stopped renting out entire homes and started to rent out a single room instead. In that case, you’d expect them to have lowered the price and changed the description. However, the price was almost never lowered. Often, the host didn’t even change the description. In some cases, the description still explicitly says that guests have the entire home to themselves.

Incidentally, this is not the first time enforcement led to a large-scale conversion of homes into rooms. This has also happened in New York.

Conclusions

With the available data, it’s not possible to know with certainty what exactly happened over the past months. That said, there are indications the agreement between Amsterdam and Airbnb may be less effective than it seemed:

There has been a decrease in the number of entire homes offered for rent more than sixty days. However, this started well before the agreement was signed. It could be a result of (the threat of) enforcement by the government itself.

After the announcement of the agreement between Amsterdam and Airbnb, hundreds of entire homes were categorised as rooms. This could be a way for hosts to evade the sixty-day cap for entire homes.

Method and data

Both Murray Cox and Tom Slee frequently scrape the Airbnb website. Cox’ data is more detailed (including, for example, the texts used in advertisements and availability information). Slee collects his data more frequently, at least so for Amsterdam. Both Cox and Slee have made their data available as open data (thanks!).

Cox and Slee are not the only ones who collect data from the Airbnb website; there are commercial providers as well. In addition, the Amsterdam Municipality has started scraping the websites of Airbnb and other platforms. It appears Amsterdam only shares this data with city council members on a confidential basis.

As for room type changes: the data probably underestimates the actual number of changes, especially for the earlier periods. The reason is that you can only detect changes if an avertisement is in both the old and the new dataset. The longer the period between two measurements, the higher turnover will be (listings disappear, new ones are added) and therefore the higher the chance of missing room type changes.

Therefore, I did an additional calculation, correcting for the amount of overlap between the old and the new measurement. The result can be seen here. The picture is slightly different, but the conclusion stands: as of the end of 2016, there was a clear increase in home-to-room changes.

I used Python for the analysis. Here’s the code. As always, comments regarding the analysis and interpretation of the data are welcome.